Systems and methods for monitoring cough

ABSTRACT

The present invention provides systems and methods for monitoring subjects, especially during sleep. Respiratory and sound data are recorded and coughs arousal are recognized as joint events in both of these signals having selected characteristics. Further, cough-arousal events during sleep are recognized when a likely cough occurs in association with a recognized EEG arousal. Cough arousal events are combined into a cough arousal index that reflects disease severity and sleep disruption due to cough. The methods of this invention are computer-implemented and can be provided as a program product including a computer readable medium. Measurements and indices provided by this invention can be used to monitor and to treat respiratory diseases.

This application is a continuation-in-part of prior application Ser. No.11/165,956 filed Jun. 24, 2005, which application claims the benefit ofU.S. provisional patent application No. 60/582,520, filed Jun. 24, 2004.This application is also a continuation-in-part of prior applicationSer. No. 10/822,260, filed Apr. 9, 2004 which application claims thebenefit of U.S. provisional applications No. 60/461,738, filed Apr. 10,2003 and 60/506,904, filed Sep. 26, 2003. All of these applications areincluded herein by reference in their entireties.

1. FIELD OF THE INVENTION

The present invention provides systems and methods for real-timephysiological monitoring, particularly of a sleeping subject in a homeenvironment, and more particularly of cough frequency and EEG arousalsduring sleep. The invention is also useful for monitoring awake and/orambulatory subjects.

2. BACKGROUND OF THE INVENTION

Cough is a frequent complaint of COPD (chronic obstructive pulmonarydisease) patients (and other patients) that can significantly impactquality of life at both a functional and a nuisance level. It isexpected that understanding cough in disease progression and treatmentwill enable more targeted treatments and better understanding of thepatient's disease experience. However, true cough frequency and itscircadian distribution remain relatively unknown because it has beendifficult to objectively quantify cough in the ‘real world environment’for a number of technical reasons leaving. Objective quantification ofcough by other routine has been difficult and time consuming for bothresearchers and subjects.

Moreover, the art lacks portable and easy-to-use monitoring methods andsystems that provide objective and quantitative data on cough and, forcough during sleep, accompanying EEG arousals. In the inventor(s)experience, no portable device has heretofore demonstrated an ability torecognize coughs and to monitor cough frequency or to provide concurrentcough and EEG data. Although a number of portable devices for assessingdaytime and night time cough have been reported, none has been reportedto assess night time cough together with its influence on sleeparchitecture as revealed by electroencephalography (EEG). See, e.g., Coxet al., 1984, An electromyographic method of objectively assessing coughintensity and use of the method to assess effects of codeine on thedose-response curve to citric acid. British Journal of ClinicalPharmacology 18: 377-382, 1984; Munyard et al., 1994, A new device forambulatory cough recording. Pediatric Pulmonology 18: 178-186, 1994; andSubburaj et al., 1996, Methods of recording and analyzing cough sounds.Pulmonary Pharmacology 9: 269-279, 1996.

Considerable confusion in the art has resulted from this lack ofobjective methods and systems for monitoring cough and sleep. On onehand, it has been previously reported that sleep suppresses cough. See,e.g., Hsu et al., Coughing frequency in patients with persistent cough:assessment using a 24 hour ambulatory recorder. European RespiratoryJournal 7: 1246-1253, 1994. Studies from EEG laboratories have reportedthat cough is almost completely absent in stage 3 and 4 sleep (deepsleep) and is further not thought to be accompanied by night timeawakenings. See, e.g., Power et al., 1984, Nocturnal cough in patientswith chronic bronchitis and emphysema. American Review of RespiratoryDisease 130: 999-1001, 1984. On the other hand, it has also beenreported that the nocturnal cough and wheezing associated with asthmamay impact sleep quality. In the study of Selby et al., 1997, Inhaledsalmeterol or oral theophylline in nocturnal asthma? American Journal ofRespiratory & Critical Care Medicine 155: 104-108, 1997, patients eitherreceived 50 μg salmeterol or individually dose-titratedsustained-release oral theophylline. Post salmeterol treatment, patientsreported an improved quality of life. The authors did observe fewernocturnal arousals, but they did not indicate whether the arousals weredue to airway obstruction or to cough. Sleep architecture did not appearto differ pre/post treatment.

On the other hand, others report that sleep in patients with a number ofsleep disorders, pulmonary disorders, and in some elderly is punctuatedwith frequent, brief arousals. The arousals are transient and generallydo not result in behavioral awakening, reoccurring in some conditions asoften as once per minute. The arousing stimulus differs in the variousdisorders and can be identified in some cases (i.e. cough, apnea, legmovements, pain), whereas in other cases (i.e. “normal” sleep ofelderly, some insomnias) it is idiopathic. EEG data during sleep revealspatients arouse to cough. Thus, multiple cough bouts over the course ofthe night yield multiple arousals and, therefore, may ultimatelyinfluence over all sleep quality. The important fact is that thearousals result in fragmented sleep rather than shortened sleep. Just aswith shortened sleep, it now is clear that sleep fragmentation leads toincreased daytime sleepiness and other deleterious effects.

This lack of objective and quantitative cough and sleep monitoringmethods and systems has thus led to confusion in the art and hashindered management of COPD, asthma, and similar conditions. Suchmethods and systems would therefore benefit medical research and medicalpractice.

A number of references are cited herein, the entire disclosures of whichare incorporated herein, in their entirety, by reference for allpurposes. Further, none of these references, regardless of howcharacterized above, is admitted as prior to the invention of thesubject matter claimed herein.

3. SUMMARY OF THE INVENTION

The objects of this invention include objective and quantitative coughmonitoring methods and systems in waking and sleeping subjects. Furthermethods and systems monitor sleep disturbance due to cough by alsoprocessing EEG data. This invention will aid in management of COPD(chronic obstructive pulmonary disease), asthma, and similar conditions(e.g., cystic fibrosis (CF)) and will also promote medical research.

The systems and methods of this invention monitor subjects and gatherrespiratory and electroencephalographic (EEG) data. This respiratorydata is processed to, inter alia, objectively recognize coughoccurrences. In controlled research environments, accuracies up to 99%have been verified by application of the methods of this invention tosubjects also observed by simultaneous video recording. Similaraccuracies are also achieved and evidenced in “real life” situations,both waking and sleeping. The EEG data is processed to, inter alia,recognize abrupt changes in frequency that reflect brief arousals(suggestive of an awake state) similar to those that can be manuallyidentified on routine polysomnograms. If electromyographic (EMG) data isavailable in an embodiment, such arousals can be corroborated by briefincreases in EMG amplitude. These arousals are brief and transient, andtherefore can cause uncertainties reading the standard 20 or 30-secondepoch sleep stage scoring system or be overlooked entirely. See, e.g.,Bonnet et al., 1992, EEG arousals: scoring rules and examples—apreliminary report from the sleep disorders atlas task force of theAmerican sleep disorders association, Sleep 15: 173-184, 1992.

The processed monitoring data is preferably then combined to determinenew clinically relevant outcome variables, the cough arousal index (CAI)and a cough disturbance index (CDI). This CAI reflects the number ofnocturnal coughs associated with an EEG arousal during each hour ofsleep. If nocturnal coughs are not associated with an EEG arousal, theyare counted in a cough disturbance index (CDI) which is defined by thenumber of coughs per hour of sleep not associated with an arousal. Thesenew indices are for medical management of individual patients and alsofor medical research, for example, for the understanding of theanti-tussive and/or pro-tussive profiles of pharmacological compounds.

In more detail, the present invention provides methods for monitoring asubject during sleep by recording respiratory and EEG data, byrecognizing the occurrences of coughs from the respiratory data, byrecognizing the occurrences of transient EEG arousals from the EEG data;and by detecting and cough-arousal event when a recognized event occursin association with a recognized EEG arousal. The methods furtherdetermine a cough arousal index as the number of cough-arousal eventsper time period during sleep. The present invention also providessystems for monitoring a subject during sleep that preferably includegarments comprising sensor for respiratory and EEG signals, and acomputer system in data communication with the garment for performingthe methods of this invention. The present invention also provides aprogram product with a computer readable medium on which is encodedinstructions for performing the methods of this invention. Furtherembodiments provides methods for use of a cough arousal index: fortreating a patient subject to cough by determining the patient cougharousal index; and administering medication in order that the patient'scough arousal index is within selected bounds; and for evaluating atherapeutic agent by administering the therapeutic agent to a subject;and monitoring the subject's cough arousal index.

This invention includes the following embodiments. In a firstembodiment, this invention includes a computer-implemented method formonitoring cough in a subject that processes tidal volume (V_(T)) dataobtained from said subject in order to recognize a respiratory eventwhen a peak-to-peak amplitude of a breath exceeds a threshold; processessound data obtained from said subject in order to recognize a soundevent when a sound envelope exceeds a threshold; processes eachrecognized event respiratory to determine if it temporally overlaps asound event and further to determine if it has an expiration-inspirationpattern characteristic of a cough; and selects as a cough event eachrespiratory event that overlaps a sound event and that has saidcharacteristic expiration-inspiration pattern.

Selected aspects of this embodiment include obtaining sound data from asensor in contact with, or in close proximity to, said subject's throat;and further processing accelerometer data obtained from said subject inorder to recognize motion of said subject; to retain said selected coughevent if no subject motion is recognized during said cough; andotherwise to discard said cough event if subject motion is recognizedduring said cough.

In a second embodiment, this invention includes a computer-implementedmethod for monitoring cough in a subject that processes respiratory dataand sound data obtained from said subject in order to recognize coughevents; processes said EEG data obtained from said subject in order torecognize transient arousal events; and detects a cough-arousal (CA)event when a recognized cough event occurs in association with arecognized EEG arousal event.

Selected aspects of this embodiment include processing accelerometerdata obtained from said subject in order to recognize motion of saidsubject; retain said selected cough event if no subject motion isrecognized during said cough; and otherwise discard said cough event ifsubject motion is recognized during said cough; and further comprisingdetermining a CA index (CAI) for a selected period of time as the numberof CA events during said selected period of time and a plurality of CAIsfor selected periods of time spanning a period of sleep of said subject.

In a third embodiment, this invention includes a computer-implementedmethod for monitoring cough in a subject that processes tidal volume(VT) data and sound data in order to recognize coughs and furtherprocesses each cough event to determine a ratio of the depth of saidcough event to a mean expiratory volume during a period of quietbreathing. Selected aspects of this embodiment then classify as a coughof cystic fibrosis if said ratio is in a range determined to becharacteristic of cystic fibrosis coughs, or as a post-infectious coughif said ratio is in a range determined to be characteristic ofpost-infectious coughs, said post-infectious range being less than saidcystic fibrosis range; or as a cough of chronic obstructive pulmonarydisease (COPD) if said ratio is in a range determined to becharacteristic of COPD coughs, said COPD range being less than saidpost-infectious range.

In a fourth embodiment, this invention includes a system for monitoringa subject during sleep having a monitoring garment comprising sensorsproviding respiratory signals, sound signals, and EEG signals from saidsubject; and a computer system comprising a computer-readable memorycomprising encoded instructions for receiving said sensor signals;processing said respiratory signals and said sound signals in order torecognize cough events; processing said EEG signals in order torecognize transient arousal events; detecting a cough-arousal (CA) eventwhen a recognized cough event occurs in association with a recognizedEEG arousal event; and determining a CA index (CAI) for a plurality ofselected time periods as the number of CA events during said selectedperiod of time.

Selected aspects of this embodiment include processing saidaccelerometer signals in order to recognize motion of said subject;retain said selected cough event if no subject motion is recognizedduring said cough; and otherwise discard said cough event if subjectmotion is recognized during said cough; and a sensor providing soundsignals is in contact with, or in close proximity to, said subject'sthroat.

This inventions also includes program products comprising computerreadable media on which are encoded instructions for practicing themethods of this invention in all their aspects. Further applications ofthis invention include methods directed to solving medical andpharmaceutical problems. For example, one such method is for treatingcough in a subject that determines cough disturbance indices (CDI) forsaid subject for selected periods of time as the number of cough eventsduring said selected periods of time, said cough events being determinedby the method of claim 1; and administers an anti-tussive therapeuticagent to said subject in order that said CDIs are within selectedbounds.

Another such method is for treating disordered sleep in a subject due tocough during sleep that determines cough arousal indices (CAI) forselected periods when the subject is sleeping as the number of cougharousal events during said selected periods of time during sleep, saidcough arousal events being determined by the method of claim 1; andadministers an anti-tussive therapeutic agent to said subject in orderthat said CAIs are within selected bounds. A further such method is forevaluating a therapeutic agent in a subject that determines prior coughdisturbance indices (CDI) for said subject for selected periods of timeadministers said therapeutic agent to said subject; determinessubsequent CDIs for said subject for further selected periods of time;and compares said prior CDIs with said subsequent CDIs to determine aneffect of said therapeutic agent on cough of said subject.

This inventions also includes further aspects of this methods andfurther embodiments that will be recognized from the followingdescription, figures, and claims.

Specific embodiments of this invention will be appreciated from thefollowing detailed descriptions and attached figures, and various of thedescribed embodiments are recited in appended claims.

4. BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be understood more fully by reference to thefollowing detailed description of preferred embodiments of the presentinvention, illustrative examples of specific embodiments of theinvention, and the appended figures in which:

FIG. 1 illustrates a wearable monitoring device and associatedprocessing system;

FIG. 2 illustrates general methods of this invention;

FIG. 3 illustrates an example of cough event detection;

FIG. 4 illustrates an example of cough-arousal detection;

FIG. 5 illustrates an example diurnal cough variability in a subjectwith COPD;

FIG. 6 illustrates an example of disturbed sleep architecture insubjects with COPD;

FIGS. 7A-B illustrate an example of the relation of the CAI index onpulmonary function and an example of the lack of a similar relation inthe prior art;

FIG. 8 illustrates an exemplary cough signal;

FIG. 9 illustrates methods of cough detection;

FIGS. 10A-B illustrate preferred filter responses;

FIG. 11 illustrates exemplary data recorded during a cough;

FIG. 12 illustrates methods of pitch determination;

FIGS. 13A-D illustrate an example of pitch determination;

FIGS. 14A-B illustrate examples of coughs in a subject with COPD;

FIGS. 15A-B illustrate examples of coughs in a subject with CF;

FIGS. 16A-B illustrate exemplary coughs in a subject withpost-infectious cough (PIC); and

FIGS. 17A and 17B illustrate a further embodiment of cough detection(threshold values are approximate).

5. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Preferred embodiments of the systems and methods of this invention aredescribed in the following. In the following, and in the application asa whole, headings are used to clarity and convenience only.

5.2 Systems and Methods of this Invention

FIG. 2 generally illustrates the methods of this invention. Briefly,these methods process and combine two separate streams of physiologicaldata in order to determine novel cough-arousal indices. Subjectrespiratory, audio, and motion data 31, after pre-processing 33, areused in an objective and automatic procedure 35 to detect occurrences ofsubject coughs. Subject electroencephalogram (EEG) and electrooculogramdata (EOG) (generally, selected electromyogram (EMG) data) 37, afterpre-processing 39, are used in an objective and automatic procedure 41to detect occurrences of subject arousals. Occurrences of recognizedcoughs and arousals are correlated 43 and the determined cough-arousaland cough-disturbance indices 45 are output. These steps andaccompanying systems are described in more detail below.

5.2.2 Recognition of Cough Events

Data processed by this invention is preferably obtained by a wearablemonitoring garment, such as garment or shirt 1 illustrated in FIG. 1,which is sufficiently comfortable and unobtrusive so that subject sleepis (substantially) not disturbed. Such a garment carries, has embedded,or integrally included sensors for gathering necessary subjectmonitoring data, and permits physiological recording during sleep in ahome setting of up to a full night's duration and/or daytime recordingsin an unrestricted ambulatory setting.

This garment is a preferred example of the monitoring equipment used toprovide data for this invention. It does not limit the invention, and inother embodiments the data processed by this invention can be gatheredby other sensor technologies known in the art, and by other dispositionsand arrangements of such sensors on the monitored subject. However, forconciseness only, the following description is largely in terms of thispreferred embodiment of the monitoring garment and associated systemcomponents.

Respiratory, audio, and motion signals 31 are obtained, respectively,from inductive plethysmographic (IP) respiratory sensor bands 5 and 7(or other sensor types providing respiratory rate and volumeinformation), one or more accelerometers and the like for sensing bodyposture and motion, for example exemplary accelerometer 11 illustratedas within the shirt, and one or more microphones for detecting coughsounds, such as throat microphone 14. Garment 1 (also referred to hereinas a “shirt”) is made of stretchable material that fits sufficientlysnugly to expand and contract with a subject's body so that embedded IPsensor bands (which, for respiratory measurement, are known asrespiratory inductive plethysmographic, or RIP, bands ) can measurecross sectional areas or circumferences of the subject's torso. One RIPband is adequate, but preferably two RIP bands are used: band 5 at thelevel of the rib cage, and band 7 at the level of the abdomen. Detailsof IP technology (and of alternative sensor technologies known in theart) are described following Sec. 5.3 and in the references includedtherein.

EMG and EOG signals 37 are obtained from EEG and EOG sensors, such assingle bipolar (parietally-located) EEG sensor 15 and single lead EOGsensor 13. The EEG and EOG sensors are preferably in electricalcommunication with shirt 1, for example by means of conductive connector17. Additional sensors, optional for this invention, may be in or incommunication with the shirt, and include pulse oximeters, capnographs,EEG electrodes (illustrated at 9 a and 9 b), and the like. In thehospital, clinic, or laboratory, other signals may be obtained from awide range of physiological sensors.

Associated locally with preferred garment 1 is local data recording unit3 operatively connected to subject sensors of the garment by data cable2 (or by short range radio link). Data recoding unit 3 is preferred forambulatory use and is preferably compact and lightweight so that it canbe worn on a belt, put in a pocket, or embedded in shirt 1. This unitstores sensor data with sufficient accuracy and precision for fullmedical disclosure and off-line analysis, and may include a touch screen(or other user input facility) for implementing a digital diary whosedata may also be transferred to the analysis computer for correlationwith the sensor readings.

The methods of this invention are implemented by analysis software thatis executed on analysis computers, such as computer 21. Analysis can bedone either concurrently with signal recording (online) or at a latertime (off line). For offline analysis, sensor data can be transferredfrom data recording unit 3 to analysis computer 21 on memory card 19,such as a compact flash card. Data may alternatively be transferred bywireless links, such as transmission using cell phone technologies andthe like. All or part of this analysis software implementing thisinvention's methods may be made available as a program product on acomputer readable medium, such as optical disk 23. For sleep monitoring,sensors carried by garment 1 can be directly linked to data analysis andstorage system 21. Alternatively, a data recording unit for use duringsleep can include additional capability and processing at the cost ofdecreased portability.

Referring again to FIG. 2, initial digitization and processing of sensordata 33 includes as necessary digitization of analog sensor signals,filtering digitized signal to remove noise and artifacts. Furtherprocessing of respiratory signals includes calibration and combinationof signals from one or more RIP bands into respiratory rate and tidalvolume (VT) signals (and, optionally, processing to remove furtherremaining artifacts), and their analysis to determine baselines andtrends. Details of the processing steps described below.

Further processing of microphone data includes identification of lowerfrequency sound components and their temporal variability which arecombined in order to recognize audio events characteristic of coughs. Ina preferred embodiment, likely cough events are then identified when thelower frequency sound components exceed determined thresholds fordetermined times. These thresholds and times are preferably adjusted toreflect the variations of individual subjects.

Next, objective, computer implemented processes 35 combine and correlatepreprocessed respiratory, audio, and motion signals in order torecognize likely cough events. These events are recognized when data isindicative of individual forceful exhalations occurring against apartially closed glottis in a single breath. In particular, a likelycough is indicated by respiratory signals with a high expiratory flowpreferably substantially above a (temporally) locally-determinedbaseline expiratory flow. Further, because a cough is an exhalationagainst a partially closed glottis, they are often associated with soundevents having lower frequency components that are substantially constantfor a certain time intervals. A further indicator of a likely cough isobservation of sound with these characteristics from processedmicrophone data. Coughs are recognized by the occurrence of acharacteristic breath event, and likely coughs are recognized by acoincidence of a characteristic breath event and a characteristic soundevent.

FIG. 3 illustrates likely coughs detected according to these methods.This figure has eight concurrent traces of, from top to bottom, a tidalvolume (V_(T)) signal, a rib-cage RIP band (RC) signal, an abdominal RIPband (AB) signal (the V_(T) is a combination of the RC and AB signals),an electrocardiogram signal (ECG), a microphone signal (MIC),occurrences of recognized sound events (EVT) (recognized from the MICsignal), occurrences of recognized coughs (CGH), and the accelerometersignal (ACC). In all traces, time increases from left to right. TheV_(T) signal is a calibrated combination of the RC and AB signals, andthe EVT signal indicates occurrences of sound events from the MICsignal.

FIG. 3 illustrates three recognized likely coughs 51 c, 53 c, and 55 c.Cough 51 c is recognized because EVT 51 b is coincident with highexpiratory flow indicated by the large negative slope 51 a in the V_(T)signal. Similarly, coughs 53 c and 55 c represent coincidences in EVTs53 b and 55 b with large negative V_(T) slopes 53 a and 55 a. EVTs 57 a,57 b, and 57 c are not recognized as coughs because they do notcorrespond negative slopes in the V_(T) signals. Finally EVT 59 a is nota cough because it corresponds to inspiration (positive slope) 59 a inthe V_(T) signal.

5.2.3 Recognition of Egg Arousals

Returning to FIG. 2, received 37 EEG and EOG signals (alternatively,selected EMG signals) are preprocessed 39 and then used to recognizetransient arousals 41. Preferred EEG sensor locations, defined using thepositioning notation common in the EEG arts, includes central bipolarplacements at C4/A1 or C3/A2, and optional bipolar occipital referentialplacements such as O1/A2, O2/A1 or OZ/A1 or A2. Preferred bipolar EOGelectrode placements are LOC/A1 and/or ROC/A2. In alternativeembodiments, the EOG signals may be supplemented or replaced bysubmental or other EMG signals.

The received signals are next digitized and preprocessed 39. TypicallyEEG and EOG signals exceeding about 50 Hz are of less interest, soadequate signal digitization is 100/sec (the Nyquist frequency); morepreferably digitization is at 150/sec or greater, and even morepreferably at 200 /sec or greater. The digitized signals are next lowpass filtered to remove less significant higher frequencies, for exampleabove about 50 Hz. Finally, the signals are processed to provide aspectrogram-type output that is reflective of signal frequency contentversus time, preferably according to the standard EEG frequency bands,namely, the alpha band, the beta band, the theta band, the delta band,and so forth. This processing can be by, for example, a bank oftime-windowed band-pass filters or a multi-resolution waveletdecomposition, where the filter pass bands or wavelet resolutions areselected according to the EEG frequency bands.

Arousals are then recognized 41 from spectrogram-type output derivedfrom either the central or occipital derivation EEG by, preferablyautomatically, applying rules derived from standard definitions of EEGarousal. See, for example, Bonnet et al., 1992, EEG arousals: scoringrules and examples—a preliminary report from the sleep disorders atlastask force of the American sleep disorders association, Sleep 15:173-184, 1992. A preferred rule recognizes arousals when the spectrogramreveals an abrupt shift in EEG frequency 3 seconds or greater durationat greater than 16 Hz (e.g., theta, alpha and/or beta frequencies) butwithout spindles. Because the 3 second criteria is primarilymethodological as opposed to physiological, other durations may be usedthat permit reliable recognition of EEG frequency shifts in thecircumstances.

This rule can be qualified according to certain subsidiary rules. Sincearousals are considered periodic phenomena disrupting sleep, onesubsidiary rule is that an arousal is recognized when a subject has beenasleep in any sleep stage for 10 or more seconds, and further that asecond arousal is recognized when 10 seconds or more of any sleep stageintervenes between a prior arousal. Generally, 10 seconds is chosenbecause determination of sleeping versus waking over an interval of lessthan 10 seconds is less reliable. However, the minimum amount ofintervening sleep necessary to score independent arousals will depend onthe background EEG and may vary in the circumstances.

A further subsidiary rule makes use of the known classification of sleepaccording to EEG characteristics into REM (rapid-eye-movement) sleep orNREM (non-REM) sleep (NREM sleep being further sub-classified into sleepstages 1, 3, 3, and 4). In NREM sleep, arousals can be recognized on thebasis of EEG characteristics alone. But because bursts of alpha or thetaEEG activity are common in REM sleep and may not reflect physiologicalarousal, reliable scoring of arousal from REM sleep preferablyadditionally requires that EOG (or EMG) amplitudes increase. However,arousals cannot be scored based solely on changes in EMG amplitude. Inessence, if REM sleep is recognized, then such EOG or EMG amplitudeincreases are required to recognize an arousal.

Further rules useful in recognizing arousals can be derived from thefurther conditions described in, for example, Bonnet et al.

5.2.4 Recognition of Cough/Arousal Events

Referred again to FIG. 2, cough-arousal events are recognized 43 duringsleep when a recognized cough 35 is detected in association with arecognized EEG arousal 41. A cough and an arousal are associated if thecough occurs during the arousal; also a cough and an arousal areassociated if the cough occurs within a time window that includes anarousal. A preferred time window precedes the arousal and has a lengthof approximately 30 sec. (or up to approximately 1 min). Another furtherpreferred time window is approximately 30 sec. (or up to approximately 1min) subsequent to the arousal. Other suitable time windows can bedetermined for individual subjects. A cough is not associated with anarousal if it does not occur during an arousal or during a time windowassociated with an arousal.

FIG. 4 illustrates an exemplary cough-arousal event. This figure haseleven concurrent traces of, from top to bottom, an EEG signal (EEG), anEOG signal (EOG), recognized arousals (ARS), a tidal volume (V_(T))signal, a rib-cage RIP band (RC) signal, a abdominal RIP band (AB)signal, a high-frequency filtered V_(T) signal (HFB), a microphonesignal (MIC), occurrences of recognized sound events (EVT), occurrencesof recognized coughs (CGH), and the accelerometer signal (ACC). In alltraces, time increases from left to right. Cough 61 d is recognized asthe coincidence of forced expiration 61 b with sound event 61 c. Andbecause cough 61 d occurs in association with (here, during) an EEGarousal 61 a, a cough-arousal event is recognized. Forces expiration 61b is more apparent at 61 b′ in the HFB signal from which low frequencycomponents have been removed. It is also preferably to monitoraccelerometer data from a subject. High pass filtering this dataprovides information on subject motion; low pass filtered data providesinformation on subject posture. Because motion and/or posture change cancause artifacts in sensor signals, it is advantageous to discard thosecoughs and/or arousals associated with motion and/or posture change

The cough-arousal index (CAI) is then determined as the number of cougharousal events (associated coughs and arousals) per hour (or per otherappropriate time period) during sleep. The cough disturbance index (CDI)is determined as the number of coughs per hour (or per other appropriatetime period) during sleep that are not part of a cough arousal event(that is, are associated with an EEG arousal). The sum of the CAI andthe CDI is the total number of coughs per hour.

These indices are output for use by the monitored subject and monitoringpersonal. For example, the monitored subject may adjust medication dosesso that the CAI is less than an acceptable threshold, or within anacceptable range so that abnormalities in the subject's sleeparchitecture are adequately reduced. Medical monitoring personnel maymonitor CAIs and CDIs of a test population in the course of drugdevelopment, testing, or evaluation.

5.2.5 Cough/Arousal Index Examples

The systems and methods of this invention, characteristics of disorderedsleep, and the clinical significance of the CAI have been ascertained bythe following measurements.

Ten patients with mild to severe COPD were monitored in their homesperforming their normal daily activities (including sleep) using theLifeShirt® monitoring system from VivoMetrics, Inc. (Ventura, Calif.).The LifeShirt system implemented the preferred monitoring garment anddata recorder described above. In particular, the monitoring garmentincluded an RC and an AB RIP band sensors, a modified limb II ECGsensor, an accelerometer sensor filtered for posture and movement, acontact microphone sensor at thyroid cartilage to identify cough sounds.During sleep, data from associated EEG and EOG sensors was alsorecorded. This physiological monitoring data was processed by thepreferred methods also described above. In addition, video (with audio)tape recordings were used to validate the preferred automatic coughrecognition. A sensitivity of 0.78, a specificity of 1.0, and anaccuracy of 0.99 were observed.

Results of these measurements include the following. First, FIG. 5illustrates mean cough frequency per hour throughout each of two days.Cough frequency followed similar circadian patterns on both days, beingcharacterized by cough frequency peaks at approximately 8:00 AM andduring approximately the 2-4:00 PM period. Nocturnal cough occurred at asignificant frequency throughout most of the night except the earlymorning. A number of these nocturnal coughs, one of which wasillustrated in FIG. 4, occurred during an EEG arousal or within apermissible time window associated with an arousal, and thus contributedto subjects' CAIs (other coughs being counted in the CDI).

Next, sleep was staged into NREM (stages 1-4) and REM sleep using thepreviously described rules to evaluate the recorded EEG signal, and thenumber of coughs during each sleep stage ascertained. FIG. 6 illustratesthese measurements: the stippled dark bars indicate the mean number ofcoughs during the sleep stages in the COPD patients; the black squaresindicate mean time duration the COPD patients spent in each sleep stage;and the open rectangles (“REF”) indicate mean time normal, healthyage-matched controls spend in each sleep stage. All values are meanswith standard errors of the means being conventionally indicated byerror bars (appearing as “I's”). This figure shows that these COPDpatients experienced cough evenly distributed throughout both stages 3and 4 of NREM sleep and also REM sleep. However, during NREM stage 1,coughs were somewhat increases; and during NREM stage 2, an exceptionalnumber of coughs occurred. Thus, nocturnal cough occurred mostfrequently during the lighter sleep stages, and hence these COPDpatients spent a greater than normal percentage of time in stage 1sleep.

Thus, nocturnal cough is likely to be preventing these COPD patient fromprogressing naturally to deeper sleep stages, leading a disruption ofsleep architecture in which an unusual percentage of time is spent instage 1 and 2 sleep. This disruption is likely to adversely affect thedaytime performance, decrease quality of life, and perhaps lead tofurther problems. This confirms the importance of monitoring andtreating nocturnal coughs in susceptible subjects.

Further, the CAI was determined for these monitored COPD patientsaccording to the previously described methods, and each patient's CAIwas correlated to that patient's percent predicted peak expiratory flow.The percent predicted expiratory flow which is the percentage ratio of apatient's FEV₁ to the FEV₁ predicted for normal, age-matched controls,is a known measure of airway obstruction useful in monitoring COPD. FIG.7A illustrates the results of this comparison: a bivariate fit ofpercent predicted peak expiratory flow with CAI shows a correlationstrength of 0.64 at a significance of 0.05. This correlation confirmsthe utility of the CAI as a clinically variable linking observedpulmonary function and sleep quality.

It is significant that the measurements and indices of this inventionare determined objectively by computer-implemented methods. Thesemeasurements do not rely on patient questioning and recollection. Incontrast, prior determinations of cough and disordered sleep have reliedon such patient recollection and reporting, both of which are known tobe unreliable. FIG. 7B illustrates another evidently unreliable priorart comparison of percent predicted peak expiratory flow with an indexof cough. The cough index used, in the absence of long term recording ofcough occurrences, was sensitivity to capsaicin, a cough inducingirritant which is an active component of peppers and used in scale ofgustatory spiciness. In comparison with FIG. 7A, which demonstrates anobjectively-determined cough cough-arousal index that stronglycorrelates with percent predicted peak expiratory flow, this figuredemonstrates no observable correlation in either COPD or asthma patientsbetween percent predicted peak expiratory flow and this cough index.

5.3 Preferred Systems and Methods

This subsection further additional details of the previously describedsystems and methods.

5.3.2 Preferred Systems

Respiratory data preferably reflects time-varying cross-sectional areasof the subject's rig cage, and also advantageously the subject'sabdomen. Techniques of signal processing and pattern recognition withreference to established physiological models (such as thetwo-compartment model of respiratory volumes) can yield indicia ormeasures of physiological functions and times of occurrences ofphysiological events. For example, it is possible to obtain respiratoryrate, tidal volume indicia, indicia of cardiac stroke volumes,occurrence times of respiratory apneas, and the like.

One preferred sensor technology fur such measurements is inductiveplethysmography (IP). This technology has been clinically confirmed toprovide reliable, semi-quantitative and quantitative data on cardiac andrespiratory functions. Briefly, IP measures the inductance of conductiveloops (generally, configured as sensor bands) that are placed at variouslevels about the thorax, abdomen, and other body structures of amonitored subject. Such time-varying loop inductance measurementsreflect primarily the time-varying cross-sectional areas enclosed bythese loops.

However, this invention is not limited to IP-based sensors, andalternative sensor technologies can be employed. Possible alternativesensor technologies make, similar to IP-based sensors, measurementsreflective of cross-sectional areas, or circumferences, or theirgeometric equivalents (for example, stress or strain), at one or morelevels through the thorax, abdomen, or other body structures. Theirsignals can be processed by methods already developed for IP sensorsignals. For example, alternative sensors can be based on thread andfabric technologies being and to be developed: a sensor may measure theresistance of conductive threads having strain-dependent resistance maybe incorporated into garments or bands; or a sensor may measure byoptical or electrical means the local strain of a fabric woven so thatlocal strain is reflective of circumferential overall strain. Foranother example, alternative sensors may use energy radiation (such asultrasound, or electric, magnetic, or electromagnetic fields) to measuregeometric parameters (such as distances) through body structures.

Other sensors may be incorporated in this invention as needed and whenavailable. These can include, for example, sensors for chemicalexposures (CO, CH₄, and the like), sensors for biological hazards(various kinds of radiation, of organisms, and the like), and othersensors. Details of IP-based wearable sensors and garments are disclosedin the sensor and garment patents and/or the cardiac function patents.

Physiological sensors are preferably disposed on monitored subjects invarious kinds of garments, for example, in bands, or in partial-shirts,or in shirts, or on partial body suits, or in full body suits, and thelike that are unobtrusive, comfortable, and non-restricting fabric. Thisinvention includes a variety of such garments and sensor dispositionstherein, the particulars of which depend primarily on the type andextent of physiological monitoring. These garments are preferablydesigned to allow sleep and/or ambulatory activities without significantdisturbance.

Details of the preferred IP technology, its disposition in garments, itsprocessing and interpretation, and certain closely allied sensortechnologies can be found in the following U.S. patents and applications(the “IP patents”) currently assigned to the current assignee of thisapplication. All of these patents and applications are incorporatedherein by reference in the entireties herein for all purposes. U.S.patents (the “sensor and garment patents”) disclosing IP technology andits disposition in fabrics and garments include, for example, U.S. Pat.Nos. 6,551,252; 6,341,504; 6,047,203; 5,331,968; 5,301,678; and4,807,640, issued Feb. 28, 1989 (stretchable IP transducer).

U.S. patents (the “data processing and interpretation patents”)disclosing processing of IP signals, for example, U.S. Pat. Nos.6,413,225; 6,015,388; 5,159,935; 4,860,766; 4,834,109; 4,815,473;4,777,962; 4,648,407; 4,373,534; and 4,308,872. Similar U.S. patentapplications includes: no. (TBD), by Coyle et al.; current attorneydocket no. 10684-035-999, filed Apr. 9, 2004; and Ser. No. 10/457,097.U.S. patents (“cardiac function patents”) disclosing processing of IPsignals to obtain measures of cardiac function include, for example,U.S. Pat. Nos. 5,588,425; 5,178,151; 5,040,540; 4,986,277; 4,456,015;and 4,452,252, and U.S. application Ser. No. 10/107,078.

5.3.3 Methods of Cough Event Recognition

Generally, these methods proceed by recognizing candidate respiratoryevents from input respiratory parameters including AB, RC, and V_(T)signals and, optionally, candidate sound events from audio input. Thencoughs events are detected from coincident combinations of candidaterespiratory events and associated candidate sound events. Types andseverity of coughs may be discriminated by the values of the respiratoryand sound event parameters.

A First Method for Cough Recognition

The first preferred method for cough detection uses only respiratorydata and is thus advantageous where sound data is not available.According to a first cough detection method, coughs must be recognizedtrue breaths with expiratory periods greater than a pre-determinedthreshold having a range of from 0.25 to 3 secs. A useful and preferredthreshold is approximately I sec, which may be individualized. Then,true breaths meeting these criteria are recognized as coughs if theirpeak expiratory flow (PEF) is greater than a pre-determined threshold ofthe running median baseline PEF value as determined from a leading, twominute window. A preferred PEF threshold is between 100 and 1000% orgreater of the running median baseline PEF value; for many subjects, aPEF threshold greater than approximately 250% results in adequate coughrecognition. The threshold can be individualized to particular subjectsusing past monitoring data.

FIG. 8 illustrates actual subject data containing coughs 94 and 98. PEFis determined from the dV/dt (labeled dVt/dt) curve as short, rapidexhalations, and in which the same two coughs 96 and 102 are readilyvisible as short sharp exhalations. In this example, PEF for cough 96 isapproximately 400% of the running median PEF baseline, while for cough102, the PEF is approximately 380% of the baseline.

A Second Method for Cough Recognition

The second method for cough detection incorporates sound input as an aidto cough detection and is preferred if sound data is not available. Inthis subsection and accompanying figures, input data and derived dataare often referred to by the following abbreviations:

-   -   RC Ribcage (RC) measurements (input data)    -   AB Abdominal (AB) measurements (input data)    -   V_(T) Tidal Volume (method input data derived as described from        the RC and AB measurements)    -   HFB High frequency band pass filtered Vt (derived data)    -   LFB Low frequency band pass filtered Vt (derived data)    -   FAB High frequency band pass filtered AB (derived data)    -   MIC Microphone audio signal recorded from a throat microphone        (input data)    -   SE Microphone audio signal envelope (derived data)    -   PITCH Maximum significant audio pitch level in a selected time        interval (derived data)    -   PITCHm Mean audio pitch level in selected time intervals        (derived data)    -   EVT Audio event and duration detector (method step)    -   CGH Cough marker (method output data indicating presence of a        detected cough)

Briefly, the Vt is first filtered into high frequency and low frequencycomponents. The AB signal is also filtered into high frequencycomponents. These further are optionally designed to further limit highfrequency noise and low frequency movement artifacts. If the filteredsignals have peak-to-peak power amplitude, or breath amplitudes (thedifference between maximum expiration and maximum inspiration) exceedinga predefined threshold, T, then both respiratory and audio signals areexamined in more detail to detect the presence of a likely cough event.If the threshold is not exceeded, a cough event is not likely.

Audio signals (from, for example, a throat microphone) are processedwith a speech recognition front-end to determine if an audio eventcontains voiced or unvoiced speech. Important to this determination isthe derived signal PITCHm, which is the mean of pitch values over afinite duration in selected bands, m. This mean level should increasesignificantly if the subject is speaking or engaged in a conversation,and not increase in the case of a cough. The pitch value is computed bymeasuring the peak-to-peak power present in the Cepsturm or MelFrequency Cepstral Coefficients (MFCCs). Another important derivedsignal is the PITCH signal. Output from audio signal processing arepulses, as illustrated by the EVT trace in FIG. 11, with timing andduration equal to that of significant audio events detected in the inputsound data.

In the absence of a sound event, no cough is detected. If a sound eventis present, its duration determines which filtered respiratory signalsshould be applied to the cough signature detector. If the duration ofthe sound event is relatively long (that is longer than the mediansignificant sound event), e.g., >=600 msec, the low frequency band passfiltered respiratory data, LFB, is analyzed by a cough signaturedetector. If the audio duration is relatively short (that is longer thanthe median signification sound event), e.g. <=600 msec., the highfrequency band pass respiratory data, HFB, is analyzed. This signalselection has been found to lead to adequate filtering of movement andmotion artifact so that cough signatures may be more clearly detected.

FIG. 9 illustrates in detail this second method for cough detection. Thetidal volume trace V_(T), which has been previously determined as thelinearly weighted sum of the RC and AB bands, is first passed through 2FIR band pass filters in parallel and the peak power (as reflected bythe maximum of the filtered signal) is measured to determine theexistence of a possible cough event if the peak power exceeds thresholdT. Filters for the input respiratory signals are preferably of thefinite impulse response (FIR) design, although infinite impulse response(IIR) filters with a minimal phase shift or time delay may be used.Here, respiratory signal phase must be sufficiently unperturbed so thatit remains temporally coincident with the corresponding audio signals.

A filter length of 1024 was determined as the preferable in order toachieve the sufficiently sharp frequency and flat phase characteristicsillustrated in FIG. 10A for the high frequency band filter in FIG. 10Bfor the low frequency band filter. Table 1 lists the parameters of thesepreferred respective filters, which have been selected to filter to theextent possible subject physical movement while retaining sufficientrespiratory movement captured from the rib cage and abdomen (RC and AB).TABLE 1 FIR filter design parameters. Stop 1 Pass 1 Stop 2 Pass 2 Stop 1Pass Stop 2 Freq Freq Freq Freq Attenuation Attenuation AttenuationSignal (Hz) (Hz) (Hz) (Hz) (dB) (dB) (dB) LFB 0.4 0.5 4.9 5.0 80 0.5 80HFB 1.0 1.1 4.9 5.0 80 0.5 80

Next the peak-to-peak power is measured and compared to a threshold. Thepeak-to-peak power is preferably taken to be the difference between themaximum on a positive going signal to the minimum on a negative goingsignal. If it meets a predetermined threshold, a candidate cough eventis considered likely present in the filtered respiratory signal. If thisthreshold is not met, a cough event is not considered likely and nofurther processing of this portion of the signal is performed. SignalsLFB, HFB, and FAB are measured to make this determination. Signal FAB isthe filter residual from the AB filtered trace, and is advantageous inthe event that RC and AB are out of phase and a have a subtractioneffect on Vt decreasing the true effort in the bands.

The threshold T is preferably selected so that normal breaths are notpassed for further examination. It can be a median or mean or othermeasure the subject's current breaths. Alternatively, a fixed thresholdcan be used. Generally, approximately 200 ml expired volume is suitablefor resting or sleeping subjects. Preferably, a fixed threshold isselected for a particular subject population or more preferably for aparticular subject, in which can a wide range of volumes may besuitable. A threshold can also be selected as a percent of the subject'scurrent expiratory volume.

The next steps process the input microphone signal (MIC). FIG. 11illustrates at an enlarged scale an exemplary sound envelope—traceSE—derived from an exemplary microphone input—trace MIC. The soundenvelope is preferably down sampled to the same sample frequency as allrespiratory bands, that is preferably 50 Hz to minimize the effects offilter residuals and derivations of the respiratory signals (alsopreferably sampled at 50 Hz). This down sampling involves averagingevery 30 samples from the microphone stream, which is initially sampledat 1500 Hz to yield the 50 Hz sound envelope. The figure alsoillustrates the determined audio event—trace EVT—and the accompanyinghigh frequency filtered Vt signal—trace HFB.

Next, the sound envelope signal is processed for audio event detectionand duration determination. The start of an audio event is recognizedwhen the sound envelope passes a threshold determined to be a selectedmultiple of the calibrated background noise threshold. Preferably, thenoise threshold is calibrated from local or long term microphonerecordings (up to 240 hours has been used). This signal is scaled to avariation of between +1 and −1 and represents a level of 30 (arbitraryunits) on the sound envelope signal scale. An advantageous eventthreshold has been found to be twice the noise threshold, or a value of60. The audio event ends when sound envelope drops back below the noisethreshold (here, a value of 60). Use of a throat microphone minimizesbackground noise. An audio event is marked in the EVT trace as a pulseof amplitude 10 (arbitrary units) and duration equal to the length ofthe audio event. If no audio event if detected, a cough is not likely tobe present and processing of this portion of the signal ends.

A cough signature is found by combining the processed respiratory andthe processed sound signals. If a possible audio event coincides with apossible respiratory event, one of these signals is selected dependingon the audio duration and further analysed for further cough signaturedetection. Having determined the duration of a significant audio coughevent either the LFB signal or HFB signal is further analyzed for thepresence of a cough signature. To select the frequency band to analyze,the audio event duration is measured. For short audio event durations,that is for events less than about 600 ms (preferably, individuallyadjusted), the HFB signal is analyzed, because shorter coughs asrevealed by the shorted sound even time are likely to have higherrespiratory frequency components (in order to expire a the shortertime). Conversely, audio events of longer time duration are likely tohave respiratory signals of lower frequency signals so that the LFBsignal is chosen for further cough signature detection.

A typical cough signature is shown in the HFB trace of FIG. 11. A coughsignature preferably has a sharp expiration (corresponding to a highpeak expiratory flow) followed by a sharp inspiration in either the HFBor LFB traces or both, that occur in association with an audio eventclassified as a cough event. The lowest sample value the HFB or LFBtraces is preferably located close to the center region of theassociated audio event. The center region is defined as those times thatare greater than 33% of the audio event duration from the start of theaudio event and less than 33% of the event duration from the event end.Furthermore, this minimum value must exceed the T value, which may beselected and calibrated based on the mean breath volume for theparticular subject (measured during times of identified quite or relaxedbreathing).

Moreover, the slopes of the HFB or LFB traces (and the gradients ofthese slopes) on either side of the minimum are preferably within thefollowing constraints. First, difference between each sample[x(n)−x(n−1)] should therefore be negative before the center of thesignature and positive after the center and before the end. Next, thesignature should be reasonably symmetrical with similar slopes on eachside of the center sample of minimum. The end points of each slope oneither side of the center sample or minimum are the points where thesignal reaches maximum amplitude before starting to decrease. These endpoints should not exceed a time duration greater than 50% of the eventtime duration past the end of the event or before the end of the event.By applying these tight constraints, the possibly of falsely detecting acough like event as a cough has been found to be reduced. Alternatively,thresholds may be specified that must be exceeded by the peak expiratoryflow and the succeeding peak inspiratory flow.

If the cough signature detector determines that a cough is not likely,further processing of this portion of the signal ends. If a coughsignature is detected, in one embodiment, the likely presence of a coughis finally output. However, in a preferred embodiment, the sound signalis further analyzed to separate cough sounds from speech sounds. Thefurther analysis converts the input audio waveform to a compactparametric representation (preferably a form of frequency versus timerepresentation) so that cough sounds may be distinguished from speechsounds, the former generally having lower frequencies and the latterhigher frequencies. Accordingly, a frequency-related threshold may bedefined in the compact representation so that signals below thethreshold are likely to be cough sounds. If the pitch exceeds what islikely for a cough, P, the event is not considered to be likely to be acough. If the pitch determination is satisfactory (less than P), thisembodiment output the likely presence of a cough.

The following summarizes these tests. A candidate event that has therespiratory signature of a cough is not considered to be a cough if theassociated sound event is determined not to include cough sounds and/orto include speech sounds. Conversely, a candidate event that has thesound signature of a cough is not a considered to be a cough if theassociated respiratory event does not have cough characteristics. Analternate test depending on pitch accepts a sound event as cough if thesignal power below the cough-speech threshold increases even if there issignal power above the cough-speech threshold. A candidate event is alsonot considered a true cough if the PITCH value is above a certainthreshold (mel-frequency threshold of 1.5-2). Even if the PITCH value isjust below this threshold, a candidate event will not be considered acough if the PITCHm value is above this threshold, where PITCHm is theaverage of all PITCH values within a predefined time duration. If theaverage of these PITCH values is above this threshold, it is impliedthat there is speech before and after this event, and therefore thisevent is probably speech.

For these further tests, the characteristics of a speech audio signalare considered to be stationary over time increments of approximately 10msec., and the pitch of the audio signal is therefore analyzed over suchsegments of such time duration. An example of the stationary portion ofa speech signal is shown in FIG. 13A (time in msec.). Even though overlonger time durations, speech signal characteristics certainly change toreflect the different audio sounds being generated, short-time spectralanalysis is a known way to so characterize audio signals.

Several techniques are known for parametrically extracting andrepresenting the pitch characteristics of an audio signal, such asLinear Prediction Coding (LPC), Mel-Frequency Cepsturm Coefficients(MFCC), and others. MFCCs have been found to be the preferable method.Generally, MFCCs are based on the known variation of the human ear'scritical bandwidths so that these coefficients are expressed in amel-frequency scale, which is linear at frequencies less than 1000 Hzand logarithmic at frequencies above 1000 Hz. These filters capture thephonetically important characteristics of speech.

MFCC Determination

FIG. 12 is a flowchart of the preferred process of computing MFCCs andis now described in this subsection. It process an audio input sampledat 1500 Hz, a sampling frequency chosen to resolve speech and coughcomponents. The first step in this process, the frame blocking step,blocks the continuous audio input signal into frames of N samples, withadjacent frames being separated by M samples (M<N). The first frameconsists of the first N samples. The second frame begins M samples afterthe first frame, and overlaps it by N−M samples. Similarly, the thirdframe begins 2M samples after the first frame (or M samples after thesecond frame) and overlaps it by N−2M samples. This process continuesuntil the entire audio has been blocked into one or more frames.Preferred blocking parameters N and M are N=64 (which is equivalent to˜40 msec. windowing and facilitates the fast radix-2 FFT) and M=32.

The windowing step windows each individual frame to minimize signaldiscontinuities at frames boundaries. Spectral distortion is minimizedby using a continuous and smooth window to taper the signal to zero atthe beginning and end of each frame. If a window is defined as w(n),0≦n≦N−1, where N is the number of samples in each frame, then the resultof windowing is the signaly ₁(n)=x ₁(n)w(n), 0 £ n £N−1   (1)The Hamming window is preferably used in this invention. It is definedas: $\begin{matrix}{{{w(n)} = {0.54 - {0.46\quad\cos\text{?}\quad\text{?}\frac{2{pn}}{N - 1}\text{?}\quad 0\quad\pounds\quad n\quad\pounds\quad N} - 1}}{\text{?}\text{indicates text missing or illegible when filed}}} & (2)\end{matrix}$

The next processing step is the Fast Fourier Transform, which convertseach frame of N samples from the time domain into the frequency domain.The FFT is a well known algorithm for implementing the discrete Fouriertransform (DFT), which is defined on the set of N samples {x_(n)}, asfollows: $\begin{matrix}{{X_{\quad n} = {\underset{\quad{k\quad = \quad 0}}{\overset{\quad{N\quad - \quad 1}}{\overset{\circ}{ì}}}\quad x_{\quad k}{\mathbb{e}}^{{- 2}\quad p\quad j\quad{{kn}/N}}}},{n = 0},1,2,\ldots\quad,{N - 1}} & (3)\end{matrix}$In general X_(n)'s are complex numbers. The resulting sequence {X_(n)}is interpreted as follows: the zero frequency corresponds to n=0,positive frequencies 0<f<F_(s)/2 correspond to values 1≦n≦N/2−1, whilenegative frequencies −F_(s)/2<f<0 correspond to N/2+1≦n−N−1. Here, F_(s)denotes the sampling frequency. The result of this step is oftenreferred to as spectrum or periodogram. FIG. 13B illustrates thespectrum or periodogram of the signal of FIG. 13A.

The next step is mel-frequency wrapping. Psychophysical studies haveshown that human perception of the frequency contents of sounds does notfollow a linear scale. Thus for each tone with an actual frequency, f,measured in Hz, a subjective pitch is measured on a scale called the‘mel’ scale, which has a linear frequency spacing below 1000 Hz and alogarithmic spacing above 1000 Hz. As a reference point, the pitch of a1 kHz tone, 40 dB above the perceptual hearing threshold, is defined as1000 mels. Therefore the following approximate formula computes mels.for a given frequency f in Hz:mel(f)=2595*log₁₀(1+f/700)   (4)

Simulating the subjective audio spectrum commonly is done by a filterbank, with filters spaced uniformly on the mel scale as illustrated inFIG. 13C. The filter bank preferably has a triangular band passfrequency response, and the spacing as well as the bandwidth isdetermined by a constant mel frequency interval. The mel-filteredspectrum of an input signal, S(ω), thus consists of the output power ofthese filters when S(ω) is the input. The number of mel spectrumcoefficients, K, is typically chosen as between 18 and 24. Note thatthis filter bank is applied in the frequency domain, therefore it simplyamounts to multiplying those triangle-shape window coefficients of FIG.13C with the time frequency spectrum of FIG. 13B. In this method, it hasbeen found preferable to apply a K=10 mel scale filter banks to theinput signal frequency spectrum due to the low sample rate.

In the final step of cepsturm determination, the log mel spectrum istransformed back to time resulting in the mel frequency cepsturmcoefficients (MFCC). The cepstral representation of the speech spectrumprovides a representation of the local spectral properties of the signalfor the given frame analysis. Because the mel spectrum coefficients (andso their logarithm) are real numbers, they can be converted to the timedomain using the Discrete Cosine Transform (DCT). Therefore if the melpower spectrum coefficients that are the result of the last step aredenoted by {tilde over (S)}_(k), k=1,2, . . . , K, the MFCC's, {tildeover (c)}_(n), may be calculated as: $\begin{matrix}{{{\text{?} = {{\underset{k = 1}{\overset{K}{\overset{\circ}{ì}}}\quad{\log( S_{k}^{\%} )}\cos\text{?}( {k - {1/2}} )\frac{np}{K}\text{?}n} = 1}},\ldots\quad,K}{\text{?}\text{indicates text missing or illegible when filed}}} & (5)\end{matrix}$Note the first component, {tilde over (c)}₀, is advantageously excludedfrom the DCT since it represents the mean value of the input signal thatcarries little speaker specific information.

FIG. 13D illustrates the cepsturm output for the speech signal alreadypresented in FIGS. 13A-C. Cough and unvoiced speech sounds have beenfound to generally fall below a me-frequency threshold of 1.5-2. It isevident that voiced speech is present in the exemplary signal becausesignal power is present above this threshold in the higher pitches. ThePITCHm signal can be obtained as a simple mean, or a power-weightedmean, or the like of the mel-frequency spectrum. The PITCH signal can beobtained as the maximum (significant or having 5% or 10% or 20% of thetotal mel power) mel-frequency cepstral coefficient resultant from thediscrete cosine transform.

Cough Severity and Classification

Optionally, detected coughs may be analyzed for severity and type. Coughseverity events can be analyzed by extracting particular characteristicsof the band pass filtered lung volume data, the LFB and HFB signals. Thecharacteristics include the depth or amplitude of the cough signatureand the reflex inspiratory drive at the end of the cough signature.Measures that allow for a discrimination of the pathological causes ofcoughs include a ratio of the depth of cough with the mean expiratoryvolume calculated on a per subject bases during identified periods ofquiet and relaxed breathing. This allows severity to be determined basedin the individual calibration and therefore aids in determining lungdisease. Further such measures include the rate of change of bothexpiratory and inspiratory volume during a cough event. Further measuresanalyze segments of the cough and compare rates of change of volume atdifferent intervals of the cough event.

In simpler cases, the amplitude of these signals (cough volume) andtheir slope (airflow rate) can be combined into diagnostic criteria forclassifying one type of cough from another. These criteria reflect, forexample, the different depth of cough and the reflex inspiratory actionat the end of the cough event. Appearance of a cough signature in theunfiltered Vt is further indicia of particular severe cough. Using thesesimpler severity criteria, it has been found that CF coughs can berecognized because they are likely to be of a higher severity; and COPDcoughs because they are likely to be of a lower severity. PIC coughs arelikely to be of an intermediate severity. Presence of a cough signaturein the unfiltered tidal volume trace Vt accompanies coughs of thehighest severity.

Further Methods for Cough Recognition

The preferred embodiments of this invention include alternative methodsfor recognizing coughs. These methods are based on and implement thefollowing general principles. First, coughs (and other discreterespiratory events) are identified by in view of concurrent sound dataand upper torso (including the thorax and/or abdomen) motions andexcursions. Upper torso motions and excursions are preferably measuredusing RIP technology, but the methods of this invention can use datameasured by other methods and technologies. Second, sound data includesthe MIC (microphone) signal itself, and preferably, also includes soundevents identified in the MIC signal. Sound events suitable for coughdetection are identified as described above (see, e.g., FIGS. 11-13 andtheir accompanying description).

Third, torso motion and sound data are analyzed and interpreted usingknown pattern classification methods can be used. These methods includerule-base systems, neural networks, and the like, but also includestatistical/machine learning methods. Briefly, statistical/machinelearning classification is based on a combination of N features fromwhich are derived a set of discriminant functions in N-dimensionalfeature space. Possible candidates for this are (but not limited to)Probability distribution estimation and clustering (Gaussian MixtureModels. Expectation-Maximization algorithm. Minimax probabilityestimation. K-means clustering.); Support Vector and other KernelMachines (Multi-class SVM classifiers. One-class SVM classifiers.Multi-class BSVM formulation trained by Kozinec's algorithm,Mitchell-Demyanov-Molozenov algorithm and Nearest Point Algorithm.Kernel Fisher Discriminant.); neural networks (back-propagationalgorithms, learning vector quantization, probabilistic neural networks,radial basis networks); boosting algorithms (adaboost).

A first method, referred to herein as “method I”, that is based on theabove principles has been described above. An alternative method,referred to herein as “method II”, is not described. Both these methodsemploy upper torso motions and excursions as measured by knownrespiratory monitoring devices. However, this is for convenience only,and other methods based on the above principles can employ otherapproaches to measuring upper torso motions and excursions.

It should be understood that in the various thresholds and thresholdvalues described below (and also in the prior description) areapproximate values useful in a particular preferred embodiment. Thesedescribed values are not limiting, and other embodiments of theinvention can use other threshold values. For example, differentmeasuring hardware will generally require different threshold values forbest cough recognition. Generally, threshold values can be determinedfor a given population from a training data set of cough data obtainedfrom the target population. Specifically, the threshold values areadjusted so that the methods best recognize actual coughs in thetraining data set. Then, the adjusted threshold values are used toanalyze further data. Also certain thresholds are measured in computerunits, referred to as “cu”. These unit are typically signal valueshaving a physiological scale determined by a calibration technique. Forexample, in the case of respiration, “cu” are stated in ml where thetidal volume measured during quiet breathing at rest (e.g., 400 ml),which are signal values.

One such alternative method, referred to as “method II”, is described inthe following, while the method previously described are referred to as“method I”. Generally, method II first identifies candidate sound events(step A) and then examines the respiratory patterns associated with thecandidate sound events (steps B-M) to determine if the candidate soundevents are likely to be coughs. Sound events are identified for thepurposes of method II similarly to how they are identified for method I.

Now, in step B, method II determines the presence of significant subjectmotion during the candidate sound event by first detecting posture fromlow-pass-filtered accelerometer signals. Then, during periods of uprightposture, motion is detected from a high-pass filtered verticalaccelerometer signal. A step count is used to determine the number oftimes the vertical axis accelerometer crosses a threshold . Thisthreshold would depend on the sample rate and calibration of theaccelerometer and is typically larger than the rest value but smallerthan values during motion. For the current design, this value is 4 cu. Asubsequent detector is used so that if there are more than 0.6 steps persecond over a chosen interval (10 s) that interval is a possiblecandidate for motion. The raw vertical axis accelerometer is filteredusing a high pass filter to derive motion only components (suggestedfilter is a 4^(th) order Butterworth II bidirectional filter with 3 dBpoint at 0.75 Hz but other filters are possible). This is then smoothedwith a 9 point Gaussian filter and the median of this final trace mustalso exceed a selected threshold to be considered motion (this thresholdvaries according to accelerometer and 100 cu was chosen here). Thisadditional check may not be necessary for a different choice ofaccelerometer. Next, in step C, breaths are detected in signals from theRC (rib cage) RIP band, in signals from the AB (abdominal) RIP band, andin the Vt (tidal volume) signal resulting from a calibrated combinationof the RC and the AB signals.

The position of the sound event in then examined in relation to thebreaths detected in the three respiratory signals. It is found whether asound event starts in inspiration or in expiration and also thepercentage of the sound event that occurs during inspiration or duringexpiration. A sound event is deemed to be “in inspiration” only if doesnot lie entirely in a breath's period of expiration. The candidate soundevent is not a cough if the sound event is “in inspiration” in all thedetected breaths (in the RC, the AB, and the Vt signals). That is, acandidate sound event is a possible cough only if at least some portionof the event is in the expiration period of at least one breath.

Although it would seem likely that a cough should occur only onexpiration, observation has indicated that a sound event can be reliablyrejected as a cough only if it occurs entirely during inspiration asseen in both RIP band traces and in the Vt trace. It is believed that,that during a cough, the respiratory bands are sufficiently disturbed bymotion artifacts that breath identification and the identification ofperiods of inspiration and expirations is less certain.

Next, in step D, the inspiratory volume associated with the sound eventis determined. If the determined inspiratory volume is less thanapproximately 1000 ml, the event is not likely to be a cough. The reasonfor this seemingly non-physiologic breath size threshold is thatfrequently large body motions are associated with a cough event whichcauses the sound event to appear to be during what appears to be aninspiration but which really is motion artifact. This misidentificationis usually identified by an abnormally large volume. Prior to or duringthis step, it is preferably that a QDC ( qualitative diagnosticcalibration) calibration be performed and that the mean inspiratoryvolume during a period of quiet, natural breathing be normalized to beapproximately 400 ml. QDC techniques are described in, e.g., U.S. Pat.Nos. 4,373,534; 4,834,109; and 6,413,225; all of which are incorporatedherein in the entireties by reference.

Next, in step E, the duration and peak of the sound event are evaluatedas to whether or not they are likely to be associated with a cough. Thepeak is the maximum of the sound event trace during the sound event.

Next, in step F, a HfAB signal is calculated from the AB RIP band signalby high-pass filtering and normalizing so that so that slopes, crossingpoints, and values can be reliably determined that are comparable amongthe various sound events. Currently, a two pass filer is preferred. Thefirst pass uses a bidirectional IIR filter based on a 5^(th) order highpass Butterworth filter with 3 dB point at 1.2 Hz. The bidirectional IIRfilter minimizes phase distortion that would be present in single passIIR filters. The second pass preferably uses a 17 coefficientSavitzky-Golay, 2nd order polynomial filter. These filters are preferredbut not limiting. Other filters that removes the strong respiratoryvariations and emphasizes the smaller, higher frequency chest wallmotions during cough can also be used. The filtered AB band signal isthat scaled (normalized) so that amplitudes are standardized. Breathdetection is performed on the scaled, filtered AB-band signal and fiveminutes of quiet breathing are identified. Breath amplitudes during thisfive minute period are set to be ˜200ml, an arbitrary value used tonormalize subsequent thresholds for a population. Note that QDCcalibration acts to calibrate a combination of RC and AB signals.

Features are extracted from the calculated HfAB signal including theminimum, the starting slope, the slope during event, and the peak-troughdeflection. Starting slope is determined from the time derivative of theHfAB signal at the initial time of the sound event. Then the number ofturning points during the event is calculated and if no turning pointsare found and if the event starts on an upstroke of HfAB (positivederivative) then the event is not a cough. Next, the minimum value ofHfAB during the sound event is determined. If the event starts on adownstroke (negative derivative) and the minimum is found at the end ofsound the event, the HfAB signal beyond the event duration (up to 2 sec)is examined to find the true minimum. The maximum between the start ofthe event and the minimum is next determined. If the maximum is at thestart of the event, the signal prior to the sound event is examined tofind the true maximum. The peak-trough deflection is the maximum valueminus the minimum value. The number of downstrokes and upstrokes duringthe event is determined to find the dominant direction during the event.

In alternative embodiments, the RC signal or both the RC and AB signalscan be used.

Next, in step G, the features extracted from the HfAB signal are testedto determine whether or not they indicate that the sound event is likelyto be a cough. The test “minimum <10000” detects the absence of turningpoints, and can be replace with the test “no turning points during eventand event begins on an upstroke”

Next, in step H, the raw MIC signal during the sound event is examinedfor shapes and features which represent a cough. Five features areextracted: feature 1-feature 5. First, a spectrum of the raw MIC signaldata during the sound event is calculate, preferably using the Welchperiodogram method with a window length of 128 samples and an overlap of120 samples. The maximum of the spectrum between 0 and 150 Hz is found,and:

feature 1: (maximum frequency)*(sample_rate)/2;

feature 2: sample number (index) of the maximum frequency.

The MIC signal is then boxcar smoothed with a preferably smoothing widthof 20 samples and with smoothing applied 4 times consecutively. Themaximum value of the smoothed MIC value is found, and:.

feature 3: the number of times the MIC signal sound drops belowmaximum/5 and rises above maximum/4.

If there is only 1 peak, then:

feature 4: time to reach 50% of the maximum value;

feature 5: (number of samples exceeding 0.4×maximum)/(total number ofsamples)

If there is more than 1 peak, then:

feature 4: All continuous periods where the raw MIC signal exceeds0.4×maximum value are recorded. The maximum width of these occurrencesis used as feature4.

feature 5: All continuous periods where the raw MIC signal exceeds0.4×maximum value are recorded. The minimum width of these occurrencesis used as feature 5

Features 3 to 5 provide an approximate quantitative description of theshape of the MIC signal (how many peaks, sharpness of peaks etc.). Otherfeatures with similar characteristics can also be used.

Next, in step J, the features extracted from the MIC signal are testedto determine whether or not they indicate that the sound event is likelyto be a cough.

Next, in step K,: Neighboring sound event prior to and subsequent to thesound event in question are searched. Specifically, a count ismaintained of the sound events that are nearest to the subject soundevent, and when this count exceeds N no more remote sound events aresearched. This search is done in the forward and reverse directions withN preferably equal to 5. The MIC signals and the corresponding ABsignals during these nearby events are evaluated as above to adaptivelyset thresholds (e.g., as the average, or median, or similar of thevalues at the nearby events) on the minimum value of HfAB and otherparameters. A preferred default value for the threshold of the minimumvalue of HfAB is −50.

Then, if the minimum value of HfAB at the current sound event is lessthan the adaptively set threshold, continue processing; otherwise thecurrent sound event is not a cough.

Next, in step L, the magnitude squared coherence function (H) betweenthe raw AB band and the vertical axis accelerometer is calculated,preferably using the Welch periodogram method. If there is significantcoherence as determined in the following test, the current event is morelikely to be due to motion and is less likely to be a cough.

Coherence is a known signal processing technique. See, e.g., Carter GC,Coherence and time delay estimation. Proc IEEE 1987, 75:236-255.Coherence is a measure of linear correlation between two signals as afunction of frequency (ref). Coherence is calculated for a 1 sec windowcentered on the center of the sound event. If there is a posture changeduring this interval no coherence is calculated and this additionalcheck is not used. For an accelerometer signal at 10 Hz and a RIP signalat 50 Hz, the RIP signal is down sampled to 10 Hz and a 64 point windowis used for the calculation with an overlap of 8 points. An arctangenttransform can also be used to normalize the variance of the estimate(optional when a fixed unit of time is used).

Then the illustrated checks are performed on the maximum coherence foundand the frequency at which that maximum occurs. Alternative checks couldemploy statistical rules for significance such as the following. Anestimated coherence value can be derived that has a particularprobability of occurrence, a, given that the true value is zero. Then,any value exceeding this level is considered to be unlikely to be afalse indication of coherence with (α×100) % confidence. This confidencelevel is given by Eα=1−(1−α)1/(N−1), where N is the number ofnon-overlapping windows used.

Finally, in step M, if the candidate sound event met the above tests andcriteria of method II, it is likely to be due to an actual a cough.

5.3.4 Cough Examples

Various types of cough signatures and preferred criteria for therediscrimination are now described in connection with FIGS. 14A-B, 15A-B,and 16A-B. Chronic obstructive pulmonary disease (COPD) generally refersto a group of pulmonary disorders that lead to progressively worseningrespiratory function. Two common causes of COPD that progressivelyimpair airflow to the lungs are bronchitis and emphysema. In chronicbronchitis, the airways are blocked and inflamed, mucus producing glandsin the bronchi are enlarged, and an excessive amount of mucus issecreted into the lungs. Therefore, this form of COPD leads to anincreased need to cough in order to clear this excessive mucus.

FIGS. 14A-B illustrate COPD coughs that were identified by the systemsand methods of this invention as implemented in a software applicationand confirmed by audio and video recording. The HFB and LFB tracesillustrate that the true cough in FIG. 14A is characterized by sharp(short duration and high airflow) expiration followed by sharpinspiration. Further an audio event was detected from throat microphoneinput that was characterized as having a low pitch and most likely toinclude cough sounds. FIG. 14B illustrates several non-cough events andone true cough event from a different COPD subject. The non-cough eventsare seen as low-pitched sound events that lacked accompanyingrespiratory cough indicia (sharp inspiration and expiration in the LFBor the HFB signals). On the other hand, the true cough event ischaracterized by associated sound and respiratory events having propercharacteristics.

Cystic Fibrosis (CF) is a life threatening multi-system condition thatprimarily affects the lungs and digestive systems. CF leads to thesecretion of sticky mucus obstructing the airways, and causing a need tocough frequently in order to try to clear the mucus from the airways.Coughing can often loosen the mucus allowing easier breathing. FIGS.15A-B illustrate coughs from two CF patients. It is apparent fromexamination of the associated traces, especially the HFB and LFB traces,that these coughs are more severe than the COPD coughs, having greateramplitudes and/or higher airflows. Furthermore, the amplitudes aresufficient so that cough signatures are readily identified in theunfiltered tidal volume (Vt) trace.

Post-infectious cough (PIC) is most common after viral infections of theupper respiratory tract. These infections can induce coughing due topersisting inflammation regardless of any increased mucus secretion.FIGS. 16A-B illustrate two examples of PIC coughs. They are seen to beof a severity intermediate between CF and COPD cough

The invention described and claimed herein is not to be limited in scopeby the preferred embodiments herein disclosed, since these embodimentsare intended as illustrations of several aspects of the invention. Anyequivalent embodiments are intended to be within the scope of thisinvention. Indeed, various modifications of the invention in addition tothose shown and described herein will become apparent to those skilledin the art from the foregoing description. Such modifications are alsointended to fall within the scope of the appended claims.

A number of references are cited herein, the entire disclosures of whichare incorporated herein, in their entirety, by reference for allpurposes. Further, none of these references, regardless of howcharacterized above, is admitted as prior to the invention of thesubject matter claimed herein.

1-47. (canceled)
 48. A computer-implemented method for monitoring coughin a subject comprising: monitoring data comprising tidal volume dataand sound data with a monitoring device responsive to said subject;processing said monitored chest wall movement consisting of movementsignals derived from the rib cage, abdomen or both; processing saidmonitored sound data obtained from said subject in order to recognize asound event when a sound envelope exceeds a threshold; processing eachsimultaneous movement and sound occurrence to identify patternscharacteristic of a cough; and identifying the event as a cough if thecharacteristic patterns are present.
 49. The method of claim 1 whereinsaid sound data is obtained from a sensor in contact with, or in closeproximity to, said subject's throat.
 50. The method of claim 1 whereinsaid sound envelope threshold is a selected multiple of calibratedbackground noise.
 51. The method of claim 1 further comprising, for eachcough event, determining a measure of pitch of said overlapping soundevent; retaining said selected cough event if said determined pitchmeasure indicates that said overlapping sound event does not includespeech sounds; and otherwise discarding said cough event if saiddetermined pitch measure does not indicate that said overlapping soundevent does not include speech sounds.
 52. The method of claim 51 whereinsaid determining pitch comprises mel frequency wrapping.
 53. The methodof claim 51 wherein said pitch measure indicates that said overlappingsound event does not include speech sounds if a maximum pitch is lessthan a cough-speech threshold.
 54. The method of claim 51 wherein saidcough-speech threshold comprises a mel-frequency threshold ofapproximately 1.5 or greater.
 55. The method of claim 51 wherein saidpitch measure indicates that said overlapping sound event does notinclude speech sounds if the signal power at pitches below acough-speech threshold increases.
 57. The method of claim 51 whereinsaid pitch measure indicates that said overlapping sound event does notinclude speech sounds if the average of the recent maximum pitches isless than a cough-speech threshold.
 58. The method of claim 1 furthercomprising, for each cough event, processing accelerometer data obtainedfrom said subject in order to recognize motion and/or change of postureof said subject; retaining said selected cough event if no subjectmotion and/or change of posture is recognized during said cough; andotherwise discarding said cough event if subject motion and/or change ofposture is recognized during said cough.